However, such parameterized variational quantum algorithms are often associated with problems of “barren plateaus” [1]}, [2]}, where training loss takes an extremely long time to update through gradients. QKL [3]}, [4]} instead provides an alternative training mechanism to use quantum states for quantum encoding and projection [5]}, and then forms a kernel to estimate a hyperplane to separate training data with its kernel alignment. We will discuss these quantum operations in detail later. Recent theoretical studies [4]}, [7]} have also proven that QKL requires less trainable parameters to archive the same performance of QNNs. These findings could potentially tackle the current challenges in low-resource SCR. In this paper, we propose a quantum kernel projection A tutorial and guidelines for QKL can be found in https://www.tensorflow.org/quantum/tutorials/quantum_data. based QML scheme that uniformly projects acoustic features in a high-dimensional space during its classical-to-quantum feature encoding phase to establish hybrid quantum-classical classification models. Figure REF  provides an example showing how to encode input acoustic features into quantum states and to capture latent representations through quantum projections in the left block for kernel metric learning [8]}, [9]}, [10]} in the right block of Figure REF . Note that quantum computing processors can be deployed in software-simulated toolkit [11]}, [12]} and noisy intermediate-scale quantum [13]} (NISQ) hardware that are currently accessible with 1 to 100 qubits.
